Decoupled Reference Governors for Multi-Input Multi-Output Systems

In this work, a computationally efficient solution for constraint management of square multi-input multi-output (MIMO) systems is presented. The solution, referred to as the Decoupled Reference Governor (DRG), maintains the highly-attractive computational features of scalar reference governors (SRG) compared to Vector Reference Governor (VRG) and Command Governor (CG). This work focuses on square MIMO systems that already achieve the desired tracking performance. The goal of DRG is to enforce output constraints and simultaneously ensure that the degradation to tracking performance is minimal. DRG is based on decoupling the input-output dynamics of the system so that every channel of the system can be viewed as an independent input-output relationship, followed by the deployment of a bank of scalar reference governors for each decoupled channel. We present a detailed set-theoretic analysis of DRG, which highlights its main characteristics. A quantitative comparison between DRG, SRG, and the VRG is also presented in order to illustrate the computational advantages of DRG. Finally, a distillation process is introduced as an example to illustrate the applicability of DRG

Performance-oriented model learning for data-driven MPC design

Model Predictive Control (MPC) is an enabling technology in applications requiring controlling physical processes in an optimized way under constraints on inputs and outputs. However, in MPC closed-loop performance is pushed to the limits only if the plant under control is accurately modeled; otherwise, robust architectures need to be employed, at the price of reduced performance due to worst-case conservative assumptions. In this paper, instead of adapting the controller to handle uncertainty, we adapt the learning procedure so that the prediction model is selected to provide the best closed-loop performance. More specifically, we apply for the first time the above "identification for control" rationale to hierarchical MPC using data-driven methods and Bayesian optimization.Comment: Accepted for publication in the IEEE Control Systems Letters (L-CSS

Multi-terminal HVDC grids with inertia mimicry capability

The high-voltage multi-terminal dc (MTDC) systems are foreseen to experience an important development in the next years. Currently, they have appeared to be a prevailing technical and economical solution for harvesting offshore wind energy. In this study, inertia mimicry capability is added to a voltage-source converter-HVDC grid-side station in an MTDC grid connected to a weak ac grid, which can have low inertia or even operate as an islanded grid. The presented inertia mimicry control is integrated in the generalised voltage droop strategy implemented at the primary level of a two-layer hierarchical control structure of the MTDC grid to provide higher flexibility, and thus controllability to the network. Besides, complete control framework from the operational point of view is developed to integrate the low-level control of the converter stations in the supervisory control centre of the MTDC grid. A scaled laboratory test results considering the international council on large electric systems (CIGRE) B4 MTDC grid demonstrate the good performance of the converter station when it is connected to a weak islanded ac grid.Peer ReviewedPostprint (author's final draft

Reference Governors for MIMO Systems and Preview Control: Theory, Algorithms, and Practical Applications

The Reference Governor (RG) is a methodology based on predictive control for constraint management of pre-stablized closed-loop systems. This problem is motivated by the fact that control systems are usually subject to physical restrictions, hardware protection, and safety and efficiency considerations. The goal of RG is to optimize the tracking performance while ensuring that the constraints are satisfied. Due to structural limitations of RG, however, these requirements are difficult to meet for Multi-Input Multi-Output (MIMO) systems or systems with preview information. Hence, in this dissertation, three extensions of RG for constraint management of these classes of systems are developed. The first approach aims to solve constraint management problem for linear MIMO systems based on decoupling the input-output dynamics, followed by the deployment of a bank of RGs for each decoupled channel, namely Decoupled Reference Governor (DRG). This idea was originally developed in my previous work based on transfer function decoupling, namely DRG-tf. This dissertation improves the design of DRG-tf, analyzes the transient performance of DRG-tf, and extends the DRG formula to state space representations. The second scheme, which is called Preview Reference Governor, extends the applicability of RG to systems incorporated with the preview information of the reference and disturbance signals. The third subject focuses on enforcing constraints on nonlinear MIMO systems. To achieve this goal, three different methods are established. In the first approach, which is referred to as the Nonlinear Decoupled Reference Governor (NL-DRG), instead of employing the Maximal Admissible set and using the decoupling methods as the DRG does, numerical simulations are used to compute the constraint-admissible setpoints. Given the extensive numerical simulations required to implement NL-DRG, the second approach, namely Modified RG (M-RG), is proposed to reduce the computational burden of NL-DRG. This solution consists of the sequential application of different RGs based on linear prediction models, each robustified to account for the worst-case linearization error as well as coupling behavior. Due to this robustification, however, M-RG may lead to a conservative response. To lower the computation time of NL-DRG while improving the performance of M-RG, the third approach, which is referred to as Neural Network DRG (NN-DRG), is proposed. The main idea behinds NN-DRG is to approximate the input-output mapping of NL-DRG with a well-trained NN model. Afterwards, a Quadratic Program is solved to augment the results of NN such that the constraints are satisfied at the next timestep. Additionally, motivated by the broad utilization of quadcopter drones and the necessity to impose constraints on the angles and angle rates of drones, the simulation and experimental results of the proposed nonlinear RG-based methods on a real quadcopter are demonstrated